code: def __init__(
      self,
      tokenizer: Union[BertTokenizer, CustomTokenizer],
      max_seq_len: int = 128,
      mode: str = "train",
  ):
      base_path = os.path.join(DATA_HOME, "lcqmc")
  
      if mode == "train":
          data_file = "train.tsv"
      elif mode == "test":
          data_file = "test.tsv"
      else:
          data_file = "dev.tsv"
      super().__init__(
          base_path=base_path,
          tokenizer=tokenizer,
          max_seq_len=max_seq_len,
          mode=mode,
          data_file=data_file,
          label_file=None,
          label_list=self.label_list,
          is_file_with_header=True,
      )
  
file_path: PaddleHub/paddlehub/datasets/lcqmc.py
imports: from typing import Union
  import os
  from paddlehub.env import DATA_HOME
  from paddlehub.utils.download import download_data
  from paddlehub.datasets.base_nlp_dataset import TextMatchingDataset
  from paddlehub.text.bert_tokenizer import BertTokenizer
  from paddlehub.text.tokenizer import CustomTokenizer
label_desc: Use image node_id 1 to create a new LCQMC object from inherited base classes: TextMatchingDataset with example: obj = LCQMC(tokenizer, max_seq_len, mode)